Choice of Trimming Proportion and Number of Clusters in Robust Clustering based on Trimming
为基于修剪的稳健聚类方法中修剪比例和聚类数的选择提供理论背景，并提出一种参数自助法来自动化参数选择，生成精简的合理参数列表供用户挑选。
So-called “classification trimmed likelihood curves” have been proposed as a heuristic tool to determine the number of clusters and trimming proportion in trimming-based robust clustering methods. However, these curves needs a careful visual inspection, and this way of choosing parameters requires subjective decisions. This work is intended to provide theoretical background for the understanding of these curves and the elements involved in their derivation. Moreover, a parametric bootstrap approach is presented in order to automatize the choice of parameter more by providing a reduced list of “sensible” choices for the parameters. The user can then pick a solution that fits their aims from that reduced list.